Field of the Disclosure
Embodiments of the present disclosure generally relate to a computer vision system, and more specifically relate to new feature point identification in sparse optical flow based tracking in a computer vision system.
Description of the Related Art
A new class of embedded safety systems, referred to as advanced driver assistance systems (ADAS), has been introduced into vehicles to reduce human operation error. Such systems may provide functionality such as rear-view facing cameras, electronic stability control, collision warning, and vision-based pedestrian detection systems. Many of these systems use a monocular camera and rely on real time computer vision processing to detect and track objects in the field of view of the camera. Optical flow based tracking is a key component in computer vision processing such as, for example, structure from motion (SfM), object detection, ego motion, video compression, and video stabilization.
One approach to optical flow based tracking that may be used in embedded safety systems is sparse optical flow based tracking. Sparse optical flow based tracking is a feature-based approach in which features, e.g., image edges, corners, etc., are identified and tracked across consecutive frames captured by a monocular camera. Given the real time processing requirements in embedded safety systems, performance improvements in aspects of sparse optical flow based tracking are desirable.